Patient Waitlist Analysis

Introduction

The dataset captures information on the total number of patients who had to wait for their treatments.

Timeframe: Data is archived and aggregated at the end of each month.

Waiting Time Bands: The dataset categorizes patients into different waiting time bands, showing the duration they had to wait for their treatments.

Patient Count: The number of patients within each waiting time band is recorded.

Categorization:

• Treatment Specialties: Data is categorized based on different medical treatment specialties (e.g., cardiology, orthopedics, among others).

• Case Type: It differentiates between various case types (e.g., inpatient, outpatient, and day case).

• Age Group: Patients are further categorized by age group, providing insights into the age distribution of waiting times.

All the files related to this project are available at Github.com/nitin6753/Dashboards/tree/main/Patient_waitlist

Summary

Generated a visually appealing and interactive dashboard to present the analysis findings.

Objective: 

• Track Patient Waiting List Status: Monitor and evaluate the current status of the patient waiting list.

• Observe Historical Trends: Analyze the monthly trends in patient waiting lists over time.

• Detailed Analysis: Perform in-depth analysis at the specialty level and by age profile.

Tasks Performed:

• • • • Data Preparation:

        • Gathered and merged data from inpatient and outpatient tables.

        • Performed data mapping to consolidate and categorize the specialties column into fewer, more manageable groups.

      • Data Analysis:

        • Calculated key metrics such as the average and median waiting list times.

        • Developed functionalities allowing users to switch between viewing average and median waiting times.

      • Visualization Enhancements:

        • Added a custom tooltip feature to display the distribution of waiting times for each specialty on a monthly basis.

        • Designed a detailed page showing the total number of cases across different time bands.

        • Implemented slicers for filtering data based on age, case type, specialty, waiting time bands